O044049498

Manikanda Prabhu. J et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 4( Version 4), April 2014, pp.94-98
www.ijera.com 94 | P a g e
Literature Survey on Voltage Profile Management Methods of
Distributed Generation
Chandragupta Mauryan. K. S1
, Manikanda Prabhu. J2
, Senapathi. N. K3
,
Madhumitha. D 4
1
Assistant Professor, 2, 3, 4
PG Scholars, Power System Engineering, Sri Krishna College of Technology,
Tamilnadu, India.
ABSTRACT
Distributed Generation (DG) has been utilized in some electrical networks in order to manage the low level
demand. Environmental and economical issues have driven significant increase in the development of
distributed generation. DG connected to the distribution systems, however, may impose negative influences with
respect to power quality and efficiency. This negative influence is based on the location of the DG in the
distributed system. In order to optimally positioning the DG in the system to improve the power quality and
efficiency many algorithms are used. In this paper we made a literature survey on different algorithms used for
positioning and sizing of DG in order to reduce losses, maintaining voltage profile. Also we have explained the
methodology of each algorithms used.
Keywords - Artificial Bee Colony, Ant Colony Optimization, Distributed Generation, Genetic Algorithm,
Optimization Algorithms.
I. INTRODUCTION
Distributed generation for a moment is
loosely defined as small scale electricity generation,
is a fairly new concept in the economics literature
about electricity markets, but the idea behind it is not
new at all. Interconnecting DG to an existing
distribution system provides various benefits for
example an enhanced power quality, higher
reliability. DG is a method of generating electricity
on a small scale from renewable and non-renewable
energy sources. DG systems are located close to
where the electricity is being used and hence the
position of DG places an important role in achieving
better efficiency. And so many optimizing algorithms
are used for optimally positioning and sizing the DG
systems. Each algorithm gives different results since
their methodology is entirely different from each
other. In this paper we have discussed about various
algorithms used for positioning and sizing of DG.
The introduction about algorithms and their
methodology have been described. This paper gives
the clear idea about the concepts of different
optimization algorithms.
II. OVERVIEW OF DISTRIBUTED
GENERATION
Before launching into an overview of
distributed generation, it is appropriate to put forward
a definition or at least an operational confine related
to distributed generation. It is generally agreed upon
that any electric power production technology that is
such that it is integrated within distribution systems
fits under the distributed generation umbrella. The
designations “distributed” and “dispersed” are used
interchangeably. The single line diagram of DG is
shown in fig.1
Fig.1: Single Line Diagram of Two Bus System
Distributed generation (or DG) generally
refers to small-scale (typically 1 kW – 50 MW)
electric power generators that produce electricity at a
site close to customers or that are tied to an electric
distribution system. Distributed generation, also
called on-site generation, dispersed
generation, embedded generation, decentralized
generation, decentralized energy, distributed energy
or district energy generates electricity from many
small energy sources.
Distributed generation is an approach that
employs small-scale technologies to produce
electricity close to the end users of power.
DG technologies often consist of modular
(and sometimes renewable-energy) generators, and
they offer a number of potential benefits. In many
cases, distributed generators can provide lower-cost
RESEARCH ARTICLE OPEN ACCESS

electricity and higher power reliability and security
with fewer environmental consequences than can
traditional power generators.
One can further categorize distributed
generation technologies as renewable and
nonrenewable.
Renewable technologies include:
Solar, photovoltaic or thermal
Wind
Geothermal
Ocean.
Nonrenewable technologies include:
Internal combustion engine, ice
Combined cycle
Combustion turbine
Micro turbines
Fuel cell.
Distributed generation should not to be
confused with renewable generation. Distributed
generation technologies may be renewable or not; in
fact, some distributed generation technologies could,
if fully deployed, significantly contribute to present
air pollution problems.
III. OPTIMIZATION ALGORITHMS
3.1 ANT COLONY OPTIMIZATION (ACO):
Since it was proposed as an optimization
method in 1991 by Dorigo et al., ACO techniques
have been attracting the attention of more researchers
[1]. The ACO algorithm is originally inspired by the
biological behavior of the ants and, specifically, their
way of communication [1]. In this algorithm the ant’s
movement is taken into account for placing of
distributed generators in the distributed side. Such
that each ants are placed at the home colony and are
allocated the next node. Also pheromone (link) is
given to each ant. The ant does not communicate
with each other directly and each follows a different
path to reach the node. Thus an n number of paths for
reaching the nodes are obtained and from that the
shortest and low cost path is chosen for optimizing.
This process is iterated until a stopping criterion is
reached where the Table.1 shows the process of
genetic algorithm.
TABLE 1: A Genetic ACO Algorithm.
In the optimal placement of DG in the
distribution network, ACO minimizes the DG
investment cost and total operating cost. It improves
quality and reliability. But the theoretical value
calculation is difficult. It is not independent and time
to convergence is uncertain.
3.2 VECTOR SWARM ALGORITHM (VSA):
VSO same as most of the evolutionary
techniques is started with an initial population
randomly and the steps of algorithm are iteratively
repeated until a termination criterion is met such as a
maximum number of generations or when there has
been no change in the solution found of the problem
for a given number of generations.
VSO algorithm can be simplified as below:
1) Initialize population of random vectors (the
parents population of the first iteration)
2) Calculate the fitness of each vector
3) Generate a new population of vectors based on
fitness children of population is obtained by changing
in the value of each dimension of each parental
population based on four operators: Participation,
Mutation, Conformation and Selection.
4) Repeat steps 2 and 3 until a termination criterion
is met.
This operator is introduced by suitable combines of
multiple vectors for problem search space, which is
consist of two sections:
a) Vector Direct Cooperation,
b) Vector Difference Cooperation.
3.3 HARMONY SEARCH ALGORITHM
(HSA):
The HSA is a new meta-heuristic population
search algorithm proposed by Geem et al, Daset al
[4]., proposed an explorative HS (EHS) algorithm to
many benchmarks problems successfully. HSA was
derived from the natural phenomena of musicians’
behavior when they collectively play their musical
instruments (population members) to come up with a
pleasing harmony (global optimal solution) [4].
The main steps of HS are as follows:
Step1-Initialize the problem and algorithm
parameters:
In this step the problem statement is
formulated and the required variables and parameters
are defined.
Step2-Initialize the harmony memory:
The initialization of the harmony memory is
made. Such that the memory space and the memory
for the defined variables and parameters are also
allocated.
Step3-Improvise a new harmony:
After the first two steps are over, the
harmony search is started and many harmonies are
STEP 1 INITIALIZATION
initialize
pheromone trail
STEP 2
SOLUTION
CONSTRUCTION
For each and
repeat solution
construction using
pheromone trail.
STEP 3
UPDATE THE
PHEROMONE
TRAIL
until stopping
criteria

generated for the defined problem. The selection of
the best harmony and rejection of the worst harmony
are also made in this step.
Step 4-Update the harmony memory:
After selecting the new harmony (best one),
the space for the new one is allocated and that of old
harmony is erased.
Step 5-Check the termination criterion:
This step is for redirecting as well as
terminating the process. A check is made to ensure
whether a best result is obtained for the stated
problem. If the result achieved satisfies the condition
then the process gets terminated or else the steps 3 &
4 are executed until the condition gets satisfied.
3.4 GENETIC ALGORITHM (GA):
Genetic algorithm is based on techniques on
principles from the genetic and evolution mechanism
observed from the natural system and living beings
[2]. In general it consists of three types of searches
1. Initial population creation
2. Evaluation of the fitness function
3. New population production
A genetic first starts with then initial
population searches, where each individual is
evaluated by its fitness function. Subsequent values
are eliminated by its fitness values. This GA leads
generation of high performing individuals. The GA
has three different types of operators. In first
operator, an individual makes performs a high fitness
value. In second operator, it selects two individuals
within generation and carries a swapping operation.
3. Third operator used to explore some of invested
point in search space, called mutation operator.
Using GA in distributed generation minimizes
the total real power losses. Also it works on both
discrete and continuous parameters. But the main
drawback is the lack of accuracy when a high quality
solution is required.
3.5 ARTIFICIAL BEE COLONY ALGORITHM
(ABC):
The artificial bee colony algorithm is a new meta-
heuristic optimization approach, introduced in 2005
by Karabog [3]. Used for both constrained and
unconstrained optimization problems and also the
results obtained in this method is better than the other
methods of optimization. The colony of artificial bees
consists of three types of bees-employed, onlookers
and scout bees [3]. The employed bees are those who
search for food sources (solutions) and share them to
the onlookers that are waiting in the dance area of the
hive. As the information are shared by means of
dancing only. The duration of the dance is
proportional to the nectar content (fitness value) of
the food source currently being exploited by the
employed bees. The dances performed by the
employed bees are watched by onlookers and scout
bees and they choose the best food source and avoids
the bad ones. Once the onlookers choose the food
source (solution) they change their status and become
employed bees. Meanwhile if the food source is fully
visited means the corresponding employed bee
becomes scout bees or onlookers. The total size of
the algorithm is divided into two halves- one half is
assigned to employed bees, in which each individual
represents the separate food source. And the other
half represents the onlookers. The entire algorithm
process comprises of three steps.
1.Finding the possible food source by sending the
employed bees and also calculating the fitness
value.
2.Selecting the best food source and rejecting the
bad ones by using the onlookers, i.e., sharing the
information between employed and onlookers.
3.Determining the scout bees and then sending them
into entirely new food source positions [3].
On using the ABC algorithm, we can
minimize the total system real power loss subject to
equality and inequality constraints. Unless other
metaheuristic algorithms, ABC has only 2 parameters
to be tuned which avoids complexity. The only
drawback is that the efficiency of the process is
confirmed only by tuning the parameters.
3.6 DIFFERENTIAL EVOLUTION
ALGORITHM (DEA):
Differential evolution (DE) is a method that
optimizes a problem by iteratively trying to improve
a candidate solution with regard to a given measure
of quality. A basic variant of the DE algorithm works
by having a population of candidate solutions (called
agents). These agents are moved around in the
search-space by using simple mathematical formulae
to combine the positions of existing agents from the
population. If the new position of an agent is an
improvement it is accepted and forms part of the
population, otherwise the new position is simply
discarded. The process is repeated and by doing so it
is hoped, but not guaranteed, that a satisfactory
solution will eventually be discovered.
IV. COMPARISON
The Table.2 shows the different algorithms
such as Ant Colony, Artificial Bee Colony, Genetic
Algorithm, Vector Swarm Algorithm, Harmony
Search Algorithm and Differential Evolution
Algorithm.

Table 2: Difference Between Various Algorithms:
SL.NO. ALGORITHM METHODOLOGY OBJECTIVE BENEFITS DRAWBACKS
1. ACO
Based on the
movement of ants. The
result generated
depends on the path
developed by the ants.
Minimizing the
DG investment
cost and total
operating cost
It improves quality
and reliability.
Theoretical value is
difficult. It is not
independent. Time
to convergence is
uncertain.
2. GA
It is a population
search technique.
Based on the fitness
value generated the
results are obtained.
Minimization of
total real power
losses.
It works on both
discrete and
continuous
parameters.
Lack of accuracy
when a high quality
solution is required.
3. VSA
It is an evolutionary
technique based on
population search. But
this population search
is different from the
GA search.
Reduces the size
of step velocity.
The convergence
rate is improved. It
controls the
movement of
particle.
More sophisticated
finite element
formulation.
4. HSA
It is the concept from
natural musical
performance process.
The solution depends
on the harmony
generated by the
musical instruments.
Minimization of
power loss.
Diversification is
controlled.
It requires more
number of
parameters.
5. ABC
It is a new concept
based on the path of
honey bees. The path
used by honey bees to
collect honey gives the
solution.
To minimize the
total system real
power loss
subject to
equality and
inequality
constraints.
Unless other
metaheuristic
algorithms, ABC
has only 2
parameters to be
tuned.
The efficiency is
confirmed by
tuning of
parameters.
6 DEA
It is a method to
optimize a problem by
maintaining a
population of
candidate solutions and
creating new candidate
solutions by combining
existing ones.
By using
Differential
Evolution (DE)
for the
placement of DG
units in electrical
distribution
systems to
reduce the power
losses and to
improve the
voltage profile.
It improves the
overall efficiency
of power system
and the
performance of
distribution system
must be improved.
DE is used for
multidimensional
real-valued
functions but does
not use the gradient
of the problem
being optimized

V. CONCLUSION
In this paper the different algorithms used
for the optimization of distributed generation in the
distribution networks are presented. The
methodology of each algorithm is described and
finally a comparison is made on the methodology and
their effect in the distributed generation. The
comparison shows us that one algorithm is superior
to one another.
REFERENCE
[1] I. A. Farhat, “Ant Colony Optimization for
Optimal Distributed Generation in
Distribution Systems”, International Journal
of Computer, Information Science and
Engineering Vol: 7 No: 8, 2013.
[2] M.Gandomkar, M.Vakilian, M.Ebsan, “A
combination of genetic algorithm and
simulated and annealing for optimal DG
allocation in distribution networks”,
CCECE/CCGEI, Saskatoon, May 2005.
[3] Fahad S. Abu-Mouti, EL-Hawary, “Optimal
Distributed Generation Allocation and
Sizing in Distribution Systems via Artificial
Bee Colony Algorithm”, IEEE Transactions
On Power Delivery, Vol. 26, No. 4, October
2011.
[4] R. Srinivasa Rao, K. Ravindra, K. Satish,
and S. V. L. Narasimham, “Power Loss
Minimization in Distribution System Using
Network Reconfiguration in the Presence of
Distributed Generation”, IEEE Transactions
On Power Systems, Vol. 28, No. 1, February
2013.
[5] Sara Molazei, Mahmoud Oukati Sadegh,
“Vector Swarm Optimization algorithm for
Distributed Generator Allocation”,
International Journal of Renewable Energy
Research Sara Molazei Et Al., Vol.3, No.1,
2013.
[6] M. Ahmadigorji, A. Abbaspour, A. Rajabi-
Ghahnavieh, M.Fotuhi- Firuzabad, “Optimal
DG placement in distribution systems using
cost/worth analysis”, World Academy of
Science Engineering and Technology 25
2009 746–753, 2009.
[7] T. Xiao-bo, W. Xue-hong, “A new method
of distributed generation optimal placement
based on load centroid”, IEEE Asia-Pacific
Power and Energy Engineering Conference
(APPEEC), pp. 1–5, 2011
[8] F. Pilo, G. Celli, S. Mocci, G. Soma, “Multi-
objective programming for optimal DG
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IEEE PES General Meeting, pp. 1–7, 2010.

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